Natural language processing (“NLP”) involves processing of a natural language input. A natural language input may be understood as a human language used by a person to interact with a computing system. NLP is used to provide assistance in a variety of domains, for instance, processing of forms to gather relevant data, processing of contracts for analyzing relevant clauses, processing of reports, processing of real-time user natural language requests, etc. An NLP based computing system may attempt to process data or perform actions based on the natural language input. However, in order to do so, the NLP system must determine the precise meaning of the natural language input such that the natural language input can be understood and acted upon by the NLP system.
Various natural language processing systems have been developed in the past. However, such natural language processing systems either lack intelligence and a technically advanced framework for determining an appropriate interpretation for a natural language input or may not be scalable owing to complexities involved in determining an accurate, complete and sufficiently nuanced interpretation. Additionally, as the complexities increase, processing time and power required for dealing with such complex inputs may also increase, and therefore available natural language processing systems may not be able to efficiently handle such complex inputs. Finally, as the coverage and sophistication of the natural language model increases, the time it requires for software development and administrative maintenance increases to the point that such systems are no longer cost effective.
For instance, NLP has traditionally been structured as a series of execution modules arranged in a pipeline, such as tokenizing, normalization, and classification. Generally, the pipelines are pre-configured and re-used wherever similar processing has been required. As NLP has grown, so has the multitude of artificial intelligence (Al) and Machine Learning (ML) models which are available to process text. Each Al or ML model typically has a targeted purpose, for example to identify a risk in a clause of a contract or to extract an employee name from full text. Each such Al or ML model requires an input to be prepared in a certain manner and may have a corresponding pipeline to provide the desired output.
The problem arises when there are a large number of such models required to fully understand a set of complex natural language text at all levels of understanding. Managing such a large number of different natural language pipelines to handle a wide variety of ways in which the natural language can be understood and processed is cumbersome and technically complicated. Additionally, the NLP of complex natural text may become more complicated and prone to errors when ML classifiers and text processors require slight variations of mostly the same input since they can only perform accurately when they receive exactly the same type of processed input for prediction as the processed input for which they were trained.
Accordingly, a technical problem with the currently available natural language processing systems is that they may be inefficient, inaccurate, and/or not scalable to large semantic models and large teams of developers.